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Contract No. DE-AC36-08GO28308

The Effectiveness of State-Level 

Policies on Solar Market 

Development in Different State 

Contexts

D. Steward, E. Doris, V. Krasko, and D. Hillman

Technical Report

NREL/TP-7A40-61029

February 2014
NREL is a national laboratory of the U.S. Department of Energy

Office of Energy Efficiency & Renewable Energy

Operated by the Alliance for Sustainable Energy, LLC

This report is available at no cost from the National Renewable Energy

Laboratory (NREL) at www.nrel.gov/publications.

Contract No. DE-AC36-08GO28308

National Renewable Energy Laboratory

15013 Denver West Parkway

Golden, CO 80401

303-275-3000 • www.nrel.gov

The Effectiveness of StateLevel Policies on Solar Market 

Development in Different State 

Contexts

D. Steward, E. Doris, V. Krasko, and D. Hillman

Prepared under Task No. SM13.1530

Technical Report

NREL/TP-7A40-61029

February 2014
NOTICE

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Acknowledgements

This work was made possible through and in support of the goals of the U.S. Department of 

Energy’s Office of Energy Efficiency & Renewable Energy solar program. The authors would 

like to recognize the interest and support of Rachel Tronstein, Chris Nichols, Kelly Knutsen, and 

Elaine Ulrich. Very helpful guidance and input on various drafts were provided by Aliza 

Wasserman, Warren Leon, Maurice Kaya, Julia Friedman, Sonya Carley, KK Duvivier, and Ken 

Gillingham. We also wish to thank our National Renewable Energy Laboratory colleagues that 

offered thoughts on multiple drafts: Marguerite Kelly, Johanna Levene, and Sarah Truitt. 

Francisco Flores’ contribution of research to the case histories is also appreciated. We also 

appreciate the technical editing of Melissa Butheau of the National Renewable Energy 

Laboratory. Any remaining errors are the responsibility of the authors. 
iv

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List of Abbreviations and Acronyms

ACEEE American Council for an Energy Efficient Economy 

ACP alternative compliance payment

ACS American Community Survey 

DG distributed generation

DSIRE Database of State Incentives for Renewables & Efficiency

FERC Federal Energy Regulatory Commission

FTG Freeing the Grid

GWh gigawatt-hour 

IOU investor-owned utility

IREC Interstate Renewable Energy Council

ITC investment tax credit

kW kilowatt

kWh kilowatt-hour 

LBNL Lawrence Berkeley National Laboratory

MW megawatt

NCUC North Carolina Utility Commission

NEG net excess generation

NNEC Network for New Energy Choices

NREL National Renewable Energy Laboratory

PPA power purchase agreement

PSC Public Service Commission

PUC Public Service Utility 

PUMS Public Microdata Sample 

PRC Public Regulatory Commission
v 

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PV photovoltaics 

REC renewable energy certificate

REPSA Renewable Energy Portfolio Standards Act 

RPS renewable portfolio standard

SDTC Solar Development Tax Credit

SREC solar renewable energy credit

TPO third-party ownership 

W Watt
vi 

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Executive Summary

In response to public interest in customer-sited distributed solar photovoltaics (PV), state and 

local policymakers have implemented policy initiatives with the goal of encouraging private 

investment and building a robust PV market. Policymakers face challenges, including limited 

budgets and incomplete information about the effectiveness of the various policy options in their 

specific situation and in crafting and executing policy that supports market development goals. 

Recent work (Krasko and Doris 2012) investigated the effect that the order in which policies are 

implemented (referred to as “policy stacking”) and the presence of low-cost enabling policies, 

such as interconnection standards and net metering, can have on the success of states in 

promoting PV markets. Findings indicate that implementation of interconnection standards and 

policy related to the valuation of excess electricity (e.g., net metering), along with indicators of 

long-term government support for a solar PV market (e.g., renewable portfolio standard [RPS]) 

and a non-policy determinant (population), explain about 70% of the variation among states in

new PV capacity. 

This paper builds on that research to determine the most effective policy strategies for different 

types of states, as determined by their physical, demographic and macroeconomic context. A 

number of researchers have investigated the effectiveness of state-level policy using various 

statistical methods to determine relationships between installed solar PV projects and policy 

initiatives (Barbose et al. 2006; Taylor 2008; Nemet 2009; Drury et al. 2011a; Drury et al. 

2011b, Burns and Kang 2012; Krasko and Doris 2012; Sarzynski et al. 2012). In this study, the 

grouping of states by non-policy factors added dimension to these analyses by identifying how 

policies function in different non-policy environments. 

In order to identify specific actionable strategies for solar policy support at the state level, states 

were first broken into four groups based on physical and demographic characteristics, then each 

group was examined statistically, as well as qualitatively, to identify successful policy strategies 

specific to those contexts. Finally, all this was discussed together to identify relevant conclusions 

for policymakers and suggest pathways for developing actionable strategies for each context. 

While many ways exist to group the states (see Section 3.1 and the Appendix), this study used 

four broad categories of background contextual factors, selecting a single, state-wide data metric 

as an indicator for each of the categories. These gross indicators were selected to identify highlevel common elements shared by the states in a group that play a role in determining the 

effectiveness of policy in supporting solar markets. While more complex strategies were 

examined, none provided substantial improvement over this broad characterization, and thus the 

simplest was used for this effort. The categories selected were: 

• Personal economic context represented by median household income (U.S. Census 2010) 

• Solar resource availability as represented by the technical potential for solar on rooftops 

(Denholm and Margolis 2008; Lopez, Roberts et al. 2012) 
vii 

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• The cost of competing grid electricity represented by a three-year average residential 

electricity price (DOE 2013) 

• General community interest in energy conservation and renewable energy represented by 

the American Council for an Energy Efficient Economy (ACEEE) Energy Efficiency 

Scorecard score (Foster et al 2012). 

The criteria used to group the states are listed in Table ES-1. The resulting groupings of states 

are shown in Figure ES-1.

Table ES-1. State Context Grouping Criteria

State Types

Expected Leader Rooftop Rich Motivated Buyer Mixed

Criteria

1. ACEEE Energy 

Efficiency Scorecard 

score ≥ average

2. Estimated technical 

potential for rooftop PV ≥

median

1. ACEEE Energy 

Efficiency Scorecard 

score < average

2. Cost of electricity < 

average

3. Income < average

4. Estimated technical 

potential for rooftop PV ≥

median

1. ACEEE Energy 

Efficiency Scorecard 

score ≥ average 

OR

 

Cost of electricity ≥

average and

Income ≥ average

States not identified in 

the previous three 

groups. These states 

have a variety of values 

for the characteristics 

evaluated.

Figure ES-1. States grouped by types of non-policy factors. Lopez et al. (2012) did not include 

rooftop solar technical potential values for Alaska and Hawaii; therefore, these states were not 

included in the groupings of states.

OR

NV

AZ

ID

UT

MT 

WY

NM

CO

NE

SD

ND

 OK

KS

 MN 

IA

MO 

AR

IL

TN 

 KY

IN

AL

OH

GA

WV

SC

PA

 VA CA 

WA

TX 

LA

WI

MS 

MI 

FL

NC

NY

ME 

 Motivated Buyers

 Mixed

 Expected Leaders

 Rooftop Rich

VT

NH

CT

MA 

RI

DE 

NJ

MD 

DC 
viii

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After grouping the states according to non-policy contextual factors, policy differences and 

differences in installed PV capacity between groups of states and among states within each group 

were compared using statistical correlations (Table ES-2). While the small sample size of the 

context-related groups of states limited the statistical certainty of connections, there were 

observable differences in the relationship between policies and solar installed capacity in each of 

the groups. For example, while RPS set-asides had a moderate correlation with increased solar

PV installation across all states, the relationship of this market-creation policy to market 

development was stronger in the expected leader and mixed groups. The expected leader group 

of states was remarkable in that all nine states in the group had an RPS, and six (50%) had long 

running solar set-asides. This early start and consistent use of mandates was correlated with

robust solar markets in the group. 

The mixed group of states had a less defined set of common policies. Six states (35%) in this 

group have an RPS, and three of those (18% of group total) have a solar set-aside. Like the states 

with a set-aside in the expected leader group, those three states are experiencing a surge in solar 

installations, reflecting the very strong correlation between solar set-aside age and solar 

installations. As their markets grow, they may also experience the robust markets seen in the 

expected leader group. 

Table ES-2. Summary of Correlation Strength between Policies and Increased Installed Solar by 

State Grouping

Policy 

Indicator

All Statesa

(n=49)

Expected 

Leaders 

(n=12)

Rooftop Rich 

(n=11)

Motivated 

Buyers (n=9) Mixed (n=17)

RPS Solar SetAside Age ** ** * * ***

FTG 2011—

Interconnection 

Score N/A N/A * * N/A

Third-Party 

Ownership 

Allowed * * N/A ** *

FTG—2011 

Net Metering 

Score * ** N/A ** N/A

Suite of 

Policies * * ** *** ** a Forty-eight states and the District of Columbia

* Moderate relationship (correlation coefficient between 0.4 and 0.6)

** Strong relationship (correlation coefficient between 0.6 and 0.8)

*** Very strong relationship (correlation coefficient between 0.8 and 1)

Another example is the development of policies that allow access through a number of different 

mechanisms (e.g., RPS, self-ownership through interconnection and net metering, allowance of 

third-party ownership) tend to result in increased development. For the motivated buyers group, 

the relationship between having a suite of policies and increased solar installations may be 

especially strong because these states have generally higher electricity prices that encourage 

buyers to take advantage of the many options available to them. 
ix

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Following this quantitative analysis, the case histories provide examples of policy strategies that 

have been successful in market support within the demographic and resource context of the 

group, and, as such, serve as potential lessons learned for states with similar non-policy contexts. 

The purpose of these is to illuminate potential strategies for the role of policy in supporting 

market development in each context. States that are generally outperforming other states or states 

that have shown marked improvement in relation to other states in the group were selected for 

case studies. Maryland, in the expected leader group, has shown rapid growth in solar 

installations in recent years, going from less than 6 megawatts (MW) in 2009 to approximately 

80 MW at the end of 2012. North Carolina is profiled for the rooftop rich group because it has 

the highest per capita installed capacity of all the states in this group despite having one of the 

highest costs for solar PV in the nation. Like North Carolina, Delaware has the highest per capita 

installed capacity for its group, and is detailed because of its aggressive promotion of solar PV. 

New Mexico was selected from the mixed group of states because its long history of enacting 

and improving existing solar policy uniquely illustrates the importance of implementing a suite 

of effective policies.

Results and observations from the quantitative analysis and case histories are detailed throughout 

the report, with major findings being: 

• Solar-related policy has a quantified effect on solar markets (as measured by installed 

capacity). 

• Non-policy factors influence the effectiveness of those policies in driving solar markets 

in situations where the non-policy factor is extreme. For example, if costs are very low 

for competing electricity, the influence of market-enabling policy, such as 

interconnection and net metering, is weakened due to reduced economic motivation. 

• Solar set-aside is most consistently correlated with increased installed capacity. This 

connection spans across all groups. However, the connection is particularly strong in 

states with lackluster non-policy market support factors. This is borne out in the mixed 

group of states, which have a lackluster, non-policy-driven market for solar, and the 

findings indicate the impact of solar set-asides is particularly strong in this group. 

• Having a suite of policies in place that are compliant with best practices is correlated with 

increased solar PV installations. 

• The case histories illustrate policy pathways specific to the non-policy contexts of the 

states in question: 

o Expected leaders. In Maryland, a comprehensive policy portfolio, with equal 

emphasis on all policy types, is driving recent market development. 

o Rooftop rich. In North Carolina, strong interest from the populous in clean 

energy-related policy distinguishes it from other members of the group. 

o Motivated buyers. The state of Delaware’s experience illustrates targeted market 

preparation and creation policy can effectively stimulate markets.

o Mixed. In New Mexico, the leading state for installed capacity in the group, 

policy diversity and strategic implementation have proven to be critical in 

effectively supporting the market. 
x 

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• Findings from the quantitative analysis and the case histories indicated there may be a 

minimum threshold of policy scope and quality that is necessary to spur solar PV 

markets. If confirmed and expanded, this finding could provide insights into the most 

effective policy development strategies for each state context and provide actionable

strategies for individual states. This is the primary focus of follow-on work to this 

research.
xi

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Table of Contents

Acknowledgements ................................................................................................................................... iii

List of Abbreviations and Acronyms ....................................................................................................... iv

Executive Summary................................................................................................................................... vi

List of Figures ........................................................................................................................................... xii

List of Tables ............................................................................................................................................. xii

1 Purpose ................................................................................................................................................. 1

2 “Types” of States.................................................................................................................................. 2

2.1 Methodology .................................................................................................................................... 2

2.2 Typology Findings and Observations .............................................................................................. 5

3 Impact of Policies Across State Groups ............................................................................................ 9

3.1 Methodology .................................................................................................................................... 9

3.1.1 Renewable Portfolio Standards with Solar Set-Asides ..................................................... 9

3.1.2 Interconnection Standards ................................................................................................. 9

3.1.3 Third-Party Ownership ..................................................................................................... 9

3.1.4 Net Metering ................................................................................................................... 10

3.1.5 Suite of Solar Policies ..................................................................................................... 10

3.2 Results/Discussion ......................................................................................................................... 10

4 Case Histories..................................................................................................................................... 13

4.1 Expected Leader Group: Maryland ................................................................................................ 14

4.1.1 State Profile: Maryland ................................................................................................... 14

4.1.2 Market Creation .............................................................................................................. 15

4.1.3 Market Preparation .......................................................................................................... 16

4.1.4 Market Expansion ........................................................................................................... 16

4.1.5 Summary ......................................................................................................................... 17

4.2 Rooftop Rich: North Carolina ........................................................................................................ 17

4.2.1 State Profile: North Carolina ........................................................................................... 18

4.2.2 Market Preparation .......................................................................................................... 18

4.2.3 Market Creation .............................................................................................................. 18

4.2.4 Market Expansion ........................................................................................................... 19

4.2.5 Summary ......................................................................................................................... 20

4.3 Motivated Buyer Group: Delaware ................................................................................................ 21

4.3.1 State Profile: Delaware ................................................................................................... 21

4.3.2 Market Creation .............................................................................................................. 21

4.3.3 Market Preparation .......................................................................................................... 23

4.3.4 Market Expansion ........................................................................................................... 23

4.3.5 Summary ......................................................................................................................... 24

4.4 Mixed Group: New Mexico ........................................................................................................... 24

4.4.1 State Profile: New Mexico .............................................................................................. 25

4.4.2 Market Creation .............................................................................................................. 25

4.4.3 Market Preparation .......................................................................................................... 26

4.4.4 Market Expansion ........................................................................................................... 27

4.4.5 Summary ......................................................................................................................... 27

5 Conclusion .......................................................................................................................................... 29

References................................................................................................................................................. 30

Appendix: Alternative Grouping Strategies Based on Non-Policy Contextual Factors .................... 33
xii

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List of Figures

Figure ES-1. States grouped by types of non-policy factors. Lopez et al. (2012) did not include 

rooftop solar technical potential values for Alaska and Hawaii; therefore, these states were not 

included in the groupings of states.................................................................................................. vii

Figure 1. Grouping of states based on non-policy factors..................................................................... 5

Figure 2. Variations in installed capacity of solar PV between the groups of states (individual state 

values are shown as colored symbols and group averages are shown as blue bars) ................. 6

Figure 3. Scatter plot showing the relationship between ACEEE scorecard score and installed 

capacity ................................................................................................................................................. 8

Figure 4. Relationship between having a “suite” of policies and installed capacity for the states 

included in the grouping analysis .................................................................................................... 12

Figure 5. States by context grouping with case history selections circled........................................ 14

Figure 6. Total installed PV capacity with policy timeline, Maryland 2004-2012................................ 17

Figure 7. Total installed PV capacity with policy timeline, North Carolina 2006-2012....................... 20

Figure 8. Total installed PV capacity with policy timeline, Delaware 1999-2012................................ 24

Figure 9. Total installed PV capacity with policy timeline, New Mexico 2003-2012 ........................... 27

Figure A-1. Units in structure—all households compared to households heated with solar energy39

Figure A-2. When structure first built—all households compared to solar heated households ...... 39

Figure A-3. State clustering ..................................................................................................................... 42

List of Tables 

Table ES-1. State Context Grouping Criteria.......................................................................................... vii

Table ES-2. Summary of Correlation Strength between Policies and Increased Installed Solar by 

State Grouping................................................................................................................................... viii

Table 1. State Context Groupings ............................................................................................................. 4

Table 2. Statistics for Criteria Used in State Context Groupings........................................................... 5

Table 3. Correlations between Non-Policy Contextual Factors and Installed Capacity ...................... 7

Table 4. Summary of Correlations between Behind-the-Meter Installed Capacity and Policy by 

State Grouping.................................................................................................................................... 11

Table 5. Quick Facts: Maryland (Expected Leader Group)................................................................... 15

Table 6. Quick Facts: North Carolina ...................................................................................................... 19

Table 7. Quick Facts: Delaware ............................................................................................................... 21

Table 8. Quick Facts: Mixed Group ......................................................................................................... 26

Table A-1. Logistic Regression Results ................................................................................................. 37

Table A-2. Group Averages with Comprehensive Grouping ................................................................ 43

Table A-3. Group Averages with Limited Grouping .............................................................................. 45
1 

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1 Purpose

Current discourse surrounding solar market development points to state policy as the primary 

driver in the U.S. market. However, without a firm understanding of how policies interact with 

the market and lead to effective market support, there is little applicability of this information for 

policymakers. Without further information about the impact of specific policies within their 

specific context, policymakers could be investing resources in a sub-optimal way. For example, a 

state policymaker may invest public resources heavily in the design and execution of a financial 

incentive policy that could be much more effective if minimal resources were concurrently 

invested in policies that approach non-financial barriers to solar market development. Knowing 

that policy is a driver for market development has limited actionable outcomes if it is not 

followed with a clear understanding of which policies work in which context. With that 

information, policymakers can make educated decisions about the appropriate investment of 

public funds. 

This report aims to move the discourse forward into a more detailed understanding of policy 

impact on solar markets by examining the influence of non-policy factors (e.g., population 

demographics, competing electricity costs) on the effectiveness of policy. The goal is to 

understand how non-policy factors influence policy effectiveness, for the purpose of identifying 

the policies that are most appropriate for states with similar non-policy characteristics. 

In order to evaluate the impact of policy in various non-policy contexts, states were allocated 

into peer groups based on shared non-policy characteristics (Section 2). Each peer group was

then analyzed empirically and statistically to determine the role of policy in driving distributed 

photovoltaic (PV) development in that context (Section 3). Section 4 presents case histories 

examining the policy history of a state in each context group to augment the statistical analysis.
2 

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2 “Types” of States

There are a variety of non-policy factors that might influence the effectiveness of PV policy at 

the state level. These factors were evaluated to develop state “typologies” or “contexts” that can 

be used to guide policy strategies in similar states (see the Appendix). The characteristics were 

selected with the goal of capturing the physical, economic, and demographic environment within 

each state relevant to the success of various policy options and policy implementation strategies. 

Grouping similar states into typologies also served to normalize, within each group of states, for 

the factors used in the selection of states for that group. Differences in solar installations among 

states in the same grouping were expected to be attributable to differences in policy. This 

analysis intentionally focused on state physical, economic, and demographic characteristics that 

were not directly related to solar PV installations but that might impact whether the state had a 

favorable or unfavorable climate for investment in behind-the-meter PV, regardless of whether 

state policy programs were in place. Section 2.1 lays out the methodology for the state typology 

and Section 2.2 outlines observations and findings from the resulting groups. 

2.1 Methodology

States were grouped based on a combination of four physical and demographic characteristics: 

• Personal economic context represented by median household income (U.S. Census 2010). 

• The physical characteristics of homes and how sunny it is in a given area represented by 

the solar rooftop potential (Denholm and Margolis 2008; Lopez, Roberts et al. 2012).1

Estimated technical potential is calculated as potential gigawatt-hour (GWh)/year for 

each state. This factor, in addition to accounting for the solar resource available in each 

state, includes an estimate of the rooftop area potentially available for PV. Because 

housing demographic factors influence the rooftop area per person (for example, 

apartment buildings have less rooftop area per resident than suburban single-family 

homes), the estimated technical potential factor captures housing type and population, 

which have been shown to be important correlating factors for solar PV (see the 

Appendix). 

• The cost of competing grid electricity represented by a three-year average residential 

electricity price (DOE 2013) 

• General community interest in energy conservation and renewable energy represented by 

the American Council for an Energy Efficient Economy (ACEEE) Energy Efficiency 

Scorecard score (Foster et al 2012). The State Energy Efficiency Scorecard score was 

selected as a proxy measurement of the state’s interest in, and progress toward, 

addressing energy conservation-related issues within the state without consideration of 

specific policies regarding solar PV. 

 

1 Lopez does not present technical potential for rooftop PV for either Alaska or Hawaii. Both states are also 

somewhat anomalous in their primary drivers for distributed solar PV, resulting in data that skews overall results. 

For example, because of its latitude, Alaska has very low capacity factors for solar in relation to the other states. 

Therefore, Alaska and Hawaii are generally excluded from the analyses presented in this report.
3 

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The four context groups were selected as follows: 

• Expected leader. States in the expected leader category were selected first, thereby 

eliminating them from further consideration for other groups. These states have excellent 

solar resource and good physical potential for residential solar installations. States in the 

expected leader category also have a higher than average ACEEE Energy Efficiency 

Scorecard score, indicating a general policy interest in energy-related issues. In general, it 

is expected these states, based on non-PV policy-related characteristics, should have high 

installed capacity of PV. 

• Rooftop rich. These were selected from the states remaining after eliminating states in 

the expected leader category. These states also have higher-than-median technical 

potential for rooftop PV but lower-than-average median income, electricity price, and 

ACEEE Scorecard score. As such, states in the rooftop rich context group have good 

solar resource availability, but lack economic motivators and demonstrated community 

interest in solar development.

• Motivated buyers. These are states with poorer-than-median technical potential for 

rooftop solar PV but might be expected to have high interest in PV due to higher-thanaverage electricity prices and income and/or a general interest in energy-related issues.

• Mixed. These states have a lower-than-average ACEEE Scorecard score but show a 

broad range of income, electricity price, and technical potential. As such, they are not 

characterized by an individual factor that would serve as clear motivation for a high rate 

of installed PV capacity. Furthermore, because states that are expected to have high PV 

penetration were already grouped in previous categories, these states would be expected 

to have lower PV penetration. 

Table 1 summarizes the resulting groupings. Summary statistics for the criteria used to develop 

the state contexts are presented in Table 2 and mapped in Figure 1. 
4 

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Table 1. State Context Groupings

State Contextsa

Expected Leader Rooftop Rich Motivated Buyers Mixed

Criteria

1. ACEEE Energy 

Efficiency Scorecard 

score ≥ average

2. Estimated technical 

potential for rooftop PV ≥ 

median

1. ACEEE Energy 

Efficiency Scorecard 

score < average

2. Cost of electricity < 

average

3. Income < average

4. Estimated technical 

potential for rooftop PV ≥ 

median

1. ACEEE Energy 

Efficiency Scorecard 

score ≥ average 

OR 

1. Cost of electricity ≥ 

average and

2. Income ≥ average

States not identified in 

the previous three 

groups. These states 

have a variety of values 

for the characteristics 

evaluated.

Arizona

California

Colorado

Illinois

Maryland

Michigan

Minnesota

New Jersey

New York

Pennsylvania

Washington

Wisconsin

Alabama

Georgia

Indiana

Louisiana

Missouri

North Carolina

Ohio

Oklahoma

South Carolina

Tennessee

Texas

Connecticut

Delaware

Iowa

Massachusetts

New Hampshire

Oregon

Rhode Island

Utah

Vermont

Arkansas

District of Columbia

Florida

Idaho

Kansas

Kentucky

Maine

Mississippi

Montana

Nebraska

Nevada

New Mexico

North Dakota

South Dakota

Virginia

West Virginia

Wyoming

a Lopez et al. (2012) did not include rooftop solar technical potential values for Alaska and Hawaii. Therefore, these 

states were not included in the groupings of states.
5 

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Figure 1. Grouping of states based on non-policy factors

Table 2. Statistics for Criteria Used in State Context Groupings

Group

Median 

Household 

Income 

(average 2010

$/year)

Residential Electricity 

Price (average $/kWh) 

2010-2012 Averagea

Technical 

Potential for 

Rooftop PV 

(median 

GWh/year)b 

ACEEE 2012 

Scorecard Score 

(average)

Expected Leader $53,714 0.118 26,866 28.4

Mixed $47,198 0.098 9,894 12.5

Motivated Buyersc $57,123 0.121 5,570 30.1

Rooftop Rich $43,467 0.102 25,274 13.3

All States $50,115 0.114 12,443 19.6 a The source of the data was the Energy Information Administration. 

b The median value was used as the criteria cutoff for the estimated technical potential for rooftop PV.

c The Motivated Buyers category includes states with ACEEE Scorecard values greater than the average of all the states 

or both higher-than-average income and higher-than-average electricity prices.

2.2 Typology Findings and Observations

A bar chart showing the variations in installed capacity (Sherwood 2013) between the four 

groups and for all states2 is presented in Figure 2. Although the group averages of per capita 

capacities appear considerably different between groups, there is a wide range of variation within

groups, resulting in inconclusive statistical significance between groups. Even so, there are 

observations that can be drawn from within the groups: 

 

2 Forty-eight states (excluding Alaska and Hawaii) and the District of Columbia

OR

NV

AZ

ID

UT

MT 

WY

NM

CO

NE

SD

ND

 OK

KS

 MN 

IA

MO 

AR

IL

TN 

 KY

IN

AL

OH

GA

WV

SC

PA

 VA CA 

WA

TX 

LA

WI

MS 

MI 

FL

NC

NY

ME 

 Motivated Buyers

 Mixed

 Expected Leaders

 Rooftop Rich

VT

NH

CT

MA 

RI

DE 

NJ

MD 

DC 
6 

This report is available at no cost from the National Renewable Energy Laboratory (NREL) at www.nrel.gov/publications.

• Within the expected leader, motivated buyer, and mixed groups, there are a broad range 

of installed capacities, indicating that policy plays a large role in whether or not markets 

are developed, even in the presence of strong non-policy contexts. 

• There is little variation in terms of installed capacity within the rooftop rich group. This 

group has good physical potential for solar, but low values for other potential non-policy 

drivers and little in terms of examples of successful markets. 

Figure 2. Variations in installed capacity of solar PV between the groups of states (individual state 

values are shown as colored symbols and group averages are shown as blue bars) 

Analyzing the correlations between non-policy contextual factors and installed capacity in each 

of the four groups helps to further understand these observations. Table 3 lists the correlation 

coefficients between non-policy factors and installed capacity for the four groups and for all 

states combined. The strength of the correlation is categorized using a simple scale based on the 

correlation coefficient for each relationship:

• Correlation coefficients < 0.2 = no relationship 

• Coefficient between 0.2 and 0.4 = weak relationship 

• Coefficient between 0.4 and 0.6 = moderate relationship 

• Coefficient between 0.6 and 0.8 = strong relationship 

• Coefficient > 0.8 = very strong relationship. 
7 

This report is available at no cost from the National Renewable Energy Laboratory (NREL) at www.nrel.gov/publications.

Table 3. Correlations between Non-Policy Contextual Factors and Installed Capacitya

Value

All Statesb Expected 

Leaders

Rooftop 

Rich

Motivated 

Buyers Mixed

ACEEE score 0.39 (W) -0.01 () 0.55 (M) 0.28 (W) 0.40 (M)

Electricity Price 0.34 (W) 0.34 (W) 0.09 () 0.26 (W) -0.06 ()

Income 0.43 (M) 0.36 (W) 0.25 (W) 0.16 () 0.35 (W)

Technical Potential 0.22 (W) 0.31 (W) 0.48 (M) -0.07 () -0.07 () a Correlation coefficients < 0.2 = none (), 0.2 to 0.4 = weak (W), 0.4 to 0.6 = moderate (M), 0.6 to 0.8 = strong (S), 

> 0.8 = very strong (VS)

b Excluding Alaska and Hawaii 

There are only weak to moderate correlations between contextual factors and installed capacity 

for all the groups. This result indicates that something other than state non-policy context drives 

the majority of differences in installed capacity between the states in each group where 

contextual factors are similar. The strength of correlations also varies in some cases between 

groups, indicating that non-policy context does make a difference, at least under some 

conditions, and those relationships are explored for the remainder of this section. 

Our analysis of the relationship between the ACEEE Scorecard score and installed PV capacity 

provided valuable insight into the influence of this potential driver on solar markets. There is a 

moderate correlation between ACEEE Scorecard score and states having a suite of solar policies 

in place. It is not surprising that states interested in energy issues in general would also be more 

likely to have adopted complimentary solar policies. A scatter plot of installed capacity and 

ACEEE Scorecard score for all states included in the grouping analysis and the District of 

Columbia (see Figure 3) reveals that states with very low ACEEE Scorecard scores (below 15), 

also have very little installed capacity. However, a high ACEEE Scorecard score (as an 

indication of a community ethic for sustainability) does not necessarily correlate with greater 

levels of solar PV installation. As expected, this result suggests there is a more complex 

relationship between general interest in energy efficiency and conservation issues and actual 

installed capacity than is revealed by analysis of a single contextual factor. 
8 

This report is available at no cost from the National Renewable Energy Laboratory (NREL) at www.nrel.gov/publications.

Figure 3. Scatter plot showing the relationship between ACEEE scorecard score and installed

capacity

While a single factor for grouping would have resulted in more clearly defined groups, the 

primary observation is that a combination of factors offers a richer view of non-policy related 

context for solar markets that is more reflective of the complex reality: 

• The relationship between the populous’ interest in clean energy (as measured by the 

ACEEE scorecard) and installed PV capacity suggests there is a threshold level of 

general interest, but even high levels of interest are not sufficient to drive solar market 

penetration alone. 

• Technical potential for rooftop PV correlates with installed capacity, indicating the 

technical potential has a moderate influence on solar markets, but only in cases where the 

technical potential is high (expected leader and rooftop rich groups). 

• Similarly, electricity price is only correlated with installed capacity in groups where 

electricity price is generally high (expected leaders and motivated buyers). 

Median household income, however, does not show the same trend in correlations with 

installed capacity. Income is weakly correlated with installed capacity even in groups of 

states where income is generally lower (e.g., the rooftop rich group) and not at all 

correlated in the highest income group (motivated buyers). It is probable that a certain 

level of economic security is needed for people to contemplate purchasing a solar PV 

system, but that once that level of income has been achieved, other factors become more 

important in the decision-making process.
9 

This report is available at no cost from the National Renewable Energy Laboratory (NREL) at www.nrel.gov/publications.

3 Impact of Policies Across State Groups

A number of researchers have investigated the effectiveness of state-level policy using various 

statistical methods to determine relationships between installed solar PV projects and policy 

initiatives (Barbose et al. 2006; Taylor 2008; Nemet 2009; Drury et al. 2011a; Drury et al. 

2011b; Burns and Kang 2012; Krasko and Doris 2012; Sarzynski et al. 2012). The grouping of 

states by non-policy factors in Section 2 partially normalizes for common non-policy factors, 

allowing for added dimension to these analyses that identifies how policies function in different 

non-policy environments. This section analyzes the impacts of policies within and across these

groupings. 

The focus of this work is on state-level policies that are intended to facilitate solar markets,

rather than those intended to provide direct financial incentive for development. Krasko and 

Doris (2012) found that market preparation (e.g., interconnection) and market creation (e.g., 

renewable portfolio standards with solar set-asides), along with non-policy factors can explain 

70% of the variation between installed capacity levels across states. While direct financial 

incentives have a role to play in development of solar markets, the focus of this study is on lowcost (to government) policies, and how they can be implemented with maximum effectiveness 

for supporting installed capacity additions. Therefore, state-level financial or tax incentives, such 

as sales tax rebates or reductions or property taxes are not included in this analysis. State-level 

tax and financial incentives are included in case studies associated with this report. 

3.1 Methodology

Statistical analysis is used to assess the effect of policy drivers on solar markets in the context of 

the groups of states. The remainder of this chapter outlines how policies are defined in the 

analyses. 

3.1.1 Renewable Portfolio Standards with Solar Set-Asides

Previous analyses found that both renewable portfolio standard (RPS) age and solar set-aside age 

correlated with increased solar installed capacity (Krasko and Doris 2012). This analysis builds 

on that work by investigating the relationship between RPS and solar set-aside age, and the nonpolicy factors used for grouping of states. For this analysis, the age of the solar set-aside (if 

present) is calculated as the number of years prior to and including 2011 in which the set-aside 

had a megawatt-hour requirement. States that have an RPS but no solar set-aside were given a 

value of 0, and states with no RPS were given a value of -1. 

3.1.2 Interconnection Standards

The 2011 Freeing the Grid (FTG) scores were used to assess interconnection standards. Although 

more recent data was available, the 2011 scores were used because 2011 was the most recent 

year for which complete solar PV capacity data was available. A higher FTG score has been 

shown to indicate policy more likely to effectively support distributed generation markets 

(Krasko and Doris 2013). 

3.1.3 Third-Party Ownership

Twenty-two states and Washington, D.C., currently specifically authorize or allow third-party 

ownership (TPO) through power purchase agreements (PPAs) or the leasing of solar PV 
10 

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systems.3 TPO is expected to be more attractive to investors in states where the value of net 

metering or other sources of production-based income, such as selling of renewable energy 

certificates (RECs), is high (Drury et al. 2011b). TPO might also be more attractive to customers 

who have lower disposable income or do not own their home because it allows for renting or 

leasing of systems. This analysis evaluates the relationship between current TPO policy,4 solar 

PV installations, and other policy drivers in the four groups of states. States that specifically 

authorize TPOs are given a score of 1. States that specifically prohibit or restrict TPOs are given 

a score of -1. States that are silent on the issue receive a score of 0. 

3.1.4 Net Metering

Forty-three states and Washington, D.C., have adopted a net-metering policy.5 FTG scores for 

net metering policies, which have previously been shown to be correlated with increased 

installed capacity nationally, are applied to the four groups in this analysis. As with 

interconnection standards, a higher FTG score is indicative of higher quality policy. 

3.1.5 Suite of Solar Policies

Having a suite of high-quality solar policies in place may also affect solar markets independently 

from the influence of any one policy. In this paper, this is analyzed by developing a simple 

scoring system for policies in each state and then adding up the scores to determine the extent to 

which the state has a suite of solar policies in place. Each of the four policy variables—FTG 

interconnection score, FTG net metering score, PPA score, and solar set-aside age—counted as 

being included in a “suite of policies” if the value of the variable for a particular state was greater 

than the national average for that variable. For example, if a state’s FTG net metering score and 

interconnection score are above the respective national averages, but the solar set-aside age and 

TPO score are below national averages, the state received a score of 2 for having a suite of 

policies. The maximum value is four because four types of policies were evaluated.

3.2 Results/Discussion

Table 4 shows significant policy correlations for each group of states. The asterisks indicate the 

strength of the correlation between the policy value and behind-the-meter solar PV installed 

capacity for each group, as described in the notes of the table. 

 

3 DSIRE. “3rd Party Solar PV Power Purchase Agreements (PPAs).” Accessed July 9, 2013: 

www.dsireusa.org/documents/summarymaps/3rd_Party_PPA_map.pdf. 4 As compiled for the Database of State Incentives for Renewables & Efficiency, which is available at 

www.dsireusa.org. 5 DSIRE. “Net Metering.” Accessed July 9, 2013: 

www.dsireusa.org/documents/summarymaps/net_metering_map.pdf. 
11 

This report is available at no cost from the National Renewable Energy Laboratory (NREL) at www.nrel.gov/publications.

Table 4. Summary of Correlations between Behind-the-Meter Installed Capacity and Policy by 

State Grouping

Policy Indicator

All 

States

Expected 

Leaders

Rooftop 

Rich

Motivated 

Buyers Mixed

RPS Solar Set-Aside Age ** ** * * ***

FTG 2011—Interconnection score N/A N/A * * N/A

Third Party Ownership Allowed * * N/A ** *

FTG—2011 Net Metering score * ** N/A ** N/A

Suite of policies * * ** *** **

Blank: No correlation or only a weak relationship (correlation coefficient < 0.4) 

* Moderate relationship (correlation coefficient between 0.4 and 0.6) 

** Strong relationship (correlation coefficient between 0.6 and 0.8) 

*** Very strong relationship (correlation coefficient between 0.8 and 1) 

Evaluating the policies within the different state context groups offers insights into how policies 

function. Nationwide, it appears that policies are only marginally correlated with increased 

installations of behind-the-meter solar. When evaluated relative to states with similar contexts, 

however, the specific policies with higher correlations become clear. Findings indicate: 

• While policy drives solar market development, the context of the state in which the 

policy is in place, in addition to the quality and type of policy in place, contribute to

policy effectiveness in the development of markets. This concept is illustrated in the 

Table 4 because the relationships between policy drivers and solar markets are different 

for the different groups of states. 

• All of the policies evaluated are influential in the motivated buyers group, and having a 

suite of policies in place is especially important in this group. The favorable non-policy 

economic attributes of these states provide conditions that encourage potential buyers to 

take advantage of diverse policy incentives. 

• One individual policy driver, solar set-aside age, while important in all groups, was 

overwhelmingly the most influential policy in the mixed group of states. A possible 

explanation for this is that in the absence of strong non-policy drivers, mandate-style 

policies may result in the most market support. 

• The expected leaders group was most correlated with RPS and net-metering policies, 

indicating that this group emphasizes both market pull strategies of valuing energy 

produced, and market push strategies of setting up mandates to be reached in order to 

develop markets. 

• Having a suite of policies in place is also moderately correlated (correlation coefficient of 

0.57) with installed capacity for all states. A scatter plot of the 2011 installed capacity 

and the number of policies with values greater than the national average (Figure 4) 

reveals a potential minimum threshold of complimentary solar policies needed to 

effectively promote solar markets. 
12 

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Figure 4. Relationship between having a “suite” of policies and installed capacity for the states 

included in the grouping analysis
13 

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4 Case Histories

Findings from the quantitative analysis illustrate the value of examining policy effectiveness 

within the confines of the context in which policy is applied. That value is the allowance for a 

better understanding of actionable policy strategies for state policymakers in a specific context. 

The contexts as defined in this paper are illustrated in Figure 5. Because of the variation in 

policies that have been applied and how they have been applied, there is discordance within the 

groups of state contexts in terms of the installed capacity within those states. By better

understanding the outliers in the groups in terms of increased capacity or innovative policy 

implementation, effective policy strategies within each group can come into focus. In this 

section, a state from each group is profiled through a policy history in order to identify specific 

strategies that could be effective within each group. The histories are organized with an overview 

of the group and the position of the state within it, followed by a history of policy development 

and implementation of market preparation, creation, and expansion policy types (Krasko and 

Doris 2012), and conclude with a summary of observations from the development of policies in 

the state. 

Major takeaways from each group are: 

• Expected leaders. In Maryland, a comprehensive policy portfolio, with equal emphasis 

on all policy types, is driving recent market development. 

• Rooftop rich. In North Carolina, strong interest from the populous in clean energyrelated policy distinguishes it from other members of the group. 

• Motivated buyers. The state of Delaware’s experience illustrates that targeted market 

preparation and creation policy can effectively stimulate PV development. 

• Mixed. In New Mexico, the leading state for installed capacity in the group, policy 

diversity and strategic implementation have proven to be critical in effectively supporting 

the market. 
14 

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Figure 5. States by context grouping with case history selections circled

Looking across the groups, there are also interesting observations from the policy histories. All 

groups provide supporting evidence for the widely held supposition that public interest in clean 

energy development drives policy development. For example, in New Mexico, much progress on 

implementation of effective policy occurred following the election of a governor who ran on a 

platform of clean energy development. This finding indicates it may be more effective for 

policymakers to capitalize on constituent demand when it is at its peak.

4.1 Expected Leader Group: Maryland

This case history focuses on the expected leader group and the policy history of Maryland. The 

expected leader group comprises twelve states that possess above-average technical potential for 

rooftop PV and have historically demonstrated public support for clean energy policy. These 

criteria generally captured states with more mature solar policy, particularly as it relates to solar 

set-asides. The expected leader group exhibits the highest correlation between non-policy drivers 

and installed PV capacity, suggesting that policy may be relatively less important in favorable 

economic or motivational climates (see Section 3), but that policy that sets a baseline allowing 

entry to a variety of players may be important to fostering market development. 

4.1.1 State Profile: Maryland

While Maryland does not lead its group in installed PV capacity, it has experienced rapid growth 

in PV adoption compared to its peers, which makes it a good candidate for study. In 2009, it was 

OR

NV

AZ

ID

UT

MT 

WY

NM

CO

NE

SD

ND

 OK

KS

 MN 

IA

MO 

AR

IL

TN 

 KY

IN

AL

OH

GA

WV

SC

PA

 VA CA 

WA

TX 

LA

WI

MS 

MI 

FL

NC

NY

ME 

 Motivated Buyers

 Mixed

 Expected Leaders

 Rooftop Rich

VT

NH

CT

MA 

RI

DE 

NJ

MD 

DC 
15 

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one of six states in the bottom half of the expected leader group with less than 6 megawatts 

(MW) of total installed capacity (GTM 2010). At the end of 2012, Maryland’s total installed 

capacity had grown to approximately 80 MW, while each of the other five states remained below 

20 MW6 (GTM 2012; IREC 2013). 

Table 5. Quick Facts: Maryland (Expected Leader Group)

Maryland

(Rank) Group Average

ACEEE Scorecarda 30.0 (4/12) 28.4

Retail Electricity (¢/kWh)b 11.6 (5/12) 11.8

Median Incomec $64,025 (1/12) $53,714

PV Technical Potential (kW/capita)d 14,850 (9/12) 26,866

Installed Capacity (W/capita)e 5.6 (6/12) 14.8 a Foster et al. 2012

b Three-year average residential electricity (EIA 2010-2012) c Median household income (U.S. Census Bureau 2010) 

d Lopez et al. 2012

e Sherwood 2013

4.1.2 Market Creation

In 2004, the Maryland state legislature adopted the Renewable Energy Portfolio Standard and 

Credit Trading Act (House Bill 1308), which required utilities to procure 7.5% of retail sales 

from renewable energy resources by 2019. In 2007, a solar set-aside was added, which is 

currently 2% by 2020. Maryland is the only state of the six with less than 6 MW of installed PV 

capacity in 2009 that has a solar set-aside as part of its RPS. This is significant because solar setaside age is the most meaningful determinant of installed PV capacity across states (Section 3). 

The growth experienced by Maryland subsequent to 2008—the first compliance year of the solar 

set-aside—is consistent with this finding.

In addition to establishing an RPS, House Bill 1308 allowed utilities to purchase renewable 

energy credits (RECs) to fulfill RPS obligations. By doing so, the legislature created additional 

value for the electricity produced by renewable energy. The act also instructed the Public Service 

Commission (PSC) to devise a market-based trading system for RECs, which can reduce 

transactional barriers and attract market participants. The PSC responded by allowing energy 

producers to list RECs available for sale on the PSC website. In 2007, when the legislature 

created the first set-aside for solar, it also created a new category of RECs specifically for solar. 

The rules for solar renewable energy credits (SREC), however, are different. To ensure Maryland 

utilities have an opportunity to purchase the SRECs, energy producers must list them on the PSC 

website for 10 days before they can sell them on the open market. This rule is unique to 

Maryland (DSIRE 2013). Furthermore, SRECs sold through the PSC require a 15-year minimum 

contract and a single, up-front payment. These policies are intended to reduce economic barriers 

by lowering initial capital requirements and reduce investment risk by requiring long-term 

contracts.

 

6 Total capacity installed for Michigan and Minnesota was approximated for 2012 using data provided by the 

Interstate Renewable Energy Council (IREC).
16 

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Having created demand for renewable energy by allowing utilities to purchase RECs and SRECs 

to meet RPS obligations, lawmakers sought to stimulate in-state supply by revising the state’s 

net-metering rules. With the adoption of House Bill 1016 in 2007, the legislature increased 

statewide aggregate capacity from 35 MW to 1,500 MW. Utilities were also required to offer net 

metering for systems up to 2 MW in size, whereas previously it was only required for systems up 

to 200 kilowatt (kW). By revising these limits, policymakers extended the benefits of PV 

development to more participants, particularly on the commercial scale. Since 2007, the year 

Freeing the Grid began evaluating state net-metering and interconnection standards, Maryland 

has received the highest possible grade for its net-metering practices (FTG 2013). 

In 2009, Maryland’s state legislature enacted a bill to allow third-party ownership of renewable 

energy generating facilities (Senate Bill 981). This was a significant occurrence because it 

allowed PV developers access to pooled capital for the first time. Third-party ownership 

structures can lower economic barriers by reducing or eliminating initial capital requirements for 

individuals. Furthermore, third-party ownership is a way that communities without suitable space 

for PV can benefit from renewable energy development.

4.1.3 Market Preparation

Prior to allowing third-party ownership, the state legislature determined it was necessary to 

improve existing utility interconnection practices to remove unnecessary barriers to renewable 

energy development. In 2007, the legislature instructed the PSC to form a task force to develop 

small generator interconnection standards that would be consistent with national best practices 

(Senate Bill 595). Following the task force’s recommendation, the PSC promulgated rules in 

2008 using Federal Energy Regulatory Commission (FERC) timelines (Case 9060 Final Order). 

These include a fast track process for smaller systems and standardized agreements for all system 

sizes.

PV developers in Maryland also enjoy strong solar easement and property rights. Property 

owners are protected from any restrictions that would result in a significant increase in system 

cost or decrease in system efficiency (House Bill 117). 

4.1.4 Market Expansion

Maryland has also implemented a variety of market expansion policies. This type of policy 

addresses higher-level barriers like technology first-costs and investment uncertainty. The 

policies that Maryland has in place include production tax credits, equipment sales tax 

exemptions, property tax exemptions, and rebate programs. 
17 

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Figure 6. Total installed PV capacity with policy timeline, Maryland 2004-2012

4.1.5 Summary

The case of Maryland demonstrates the importance of comprehensive policy. Prior to 2010, it 

was one of six states in the expected leader group that was significantly behind its peers in terms 

of installed PV capacity. While all six have grown in the period since, Maryland is approaching 

80 MW of installed capacity, while none of the other five states have reached 20 MW. It is 

difficult to point to a single policy to explain Maryland’s recent growth. However, Maryland 

possesses characteristics that are consistent with the determinants for success identified in 

Section 3. For instance, Maryland is the only one of the six that has a solar set-aside as part of its 

RPS, the attribute found to be the most meaningful determinant of installed capacity across states 

of all the factors analyzed. In addition, the data suggest that allowing third-party ownership may 

have stimulated the recent growth because the nonresidential sector accounts for most of the 

capacity that has been installed since 2009. The Maryland example illustrates how a portfolio of 

best practice-based policies can support market growth in this peer group. 

4.2 Rooftop Rich: North Carolina

This case history focuses on the rooftop rich group and the policy history of North Carolina. The 

rooftop rich group includes states that have above-average technical potential for rooftop PV, but 

lack favorable economic conditions to stimulate demand for PV technology. The use of these 

criteria resulted in a peer group comprising 11 members. 

Within the rooftop rich group, installed PV capacity is most highly correlated to the ACEEE 

Scorecard score of the non-policy drivers. Economic factors, such as electricity price and income 

level, have a low correlation with installed PV capacity in the group. Among policy drivers, solar 

set-aside age shows the strongest relationship to installed capacity. These findings suggest policy 
18 

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maturity and public support can be important drivers of PV adoption in the absence of a 

favorable economic environment (see Section 3 for details). 

4.2.1 State Profile: North Carolina

North Carolina has the highest installed PV capacity per person in the rooftop rich group, despite 

having one of the highest installed costs in the nation for systems under 10 kW: $6.60/Watt (W)

(Barbose et al., 2012). The state also has the highest ACEEE Scorecard score within its group. 

These characteristics are consistent with the finding that higher interest and public support for 

energy-related issues is a driver of PV adoption. 

4.2.2 Market Preparation

In the five-year period from 2007 to 2012, North Carolina implemented a series of policies 

intended to foster energy efficiency and renewable energy in the state. The majority of these 

policies were the direct result of Senate Bill 3 (Senate Bill 3) enacted in 2007. Senate Bill 3 

contained a variety of policies designed to address market preparation, creation, and expansion. 

In addition to establishing a RPS, Senate Bill 3 instructed the North Carolina Utilities 

Commission (NCUC) to promulgate interconnection standards for renewable energy facilities up 

to 10 kW in size and also to consider adopting rules that would require utilities to offer net 

metering for facilities with capacities of 1 MW or less. In June 2008, NCUC issued 

interconnection standards based on the guidelines recommended by the FERC in Order No. 

2006. These standards included solar-friendly provisions, such as a fast-track application for 

systems smaller than 2 MW in size and no limits on individual system size. The following year, 

NCUC revised existing net-metering regulations to remove limits on aggregate customer 

participation and added a prohibition of stand-by charges for residential systems up to 20 kW 

(Docket No. E-100, Sub 83). 

4.2.3 Market Creation 

Establishing an RPS was one of the primary ways in which the legislature sought to promote 

renewable energy development through Senate Bill 3. While not the first state to adopt an RPS, 

North Carolina’s initial implementation was more comprehensive than the first attempts of many 

others. It included provisions, such as a set-aside for solar, and standards for municipalities and 

cooperatives. Such provisions have frequently been added to more mature RPSs via subsequent 

amendment. Immediate adoption of a solar set-aside is a significant occurrence because it can be 

the most meaningful determinant of installed PV capacity of all the factors they analyzed. 

Indeed, it was also the most relevant of policy-related factors within the rooftop rich group. 
19 

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Table 6. Quick Facts: North Carolina

North Carolina

(Rank)

Group 

Average

ACEEE Scorecard 19.5 (1/11) 13.3

Retail Electricity (¢/kWh) 10.4 (5/11) 10.2

Median Income $43,753 (6/11) $43,467

PV Technical Potential (kW/capita) 28,420 (4/11) 25,274

Installed Capacity (W/capita) 1.6 (1/11) 0.8 a Foster et al. 2012

b Three-year average residential electricity (EIA 2010-2012) c Median household income (U.S. Census Bureau 2010)

d Lopez et al. 2012

e Sherwood 2013

Investor-owned utilities (IOU) are required to offer net metering for systems up to 1 MW in size, 

and there is no limit on aggregate capacity. By allowing a range of system sizes to net meter and 

requiring utilities to accept all system owners who qualify, there is significant potential for 

participation. However, this potential is governed by the treatment of net excess generation 

(NEG). NEG is allowed to carry forward to subsequent billing periods for 12 months at the 

utility’s full retail rate. While this is favorable for the customer, all unused NEG is forfeited to 

the utility at the end of the annual billing cycle without compensation. This practice has potential 

benefits for the utility program implementer, but may act as a disincentive to conserve electricity 

when the user is faced with a possible surplus. 

To ensure compliance with RPS requirements, the NCUC established a REC program to track 

production. Through this program, utilities are allowed to purchase RECs as a means of fulfilling 

RPS requirements without having to develop their own renewable energy generation facilities. 

This has led to the development of two markets for REC transactions. The primary way in which 

utilities acquire RECs is by purchasing them directly from PV system owners. This typically 

occurs within the context of a long-term contract, whereby the producer receives an up-front 

payment based on system size and a stream of payments tied to electricity production. REC 

procurement programs can reduce the economic barriers of PV development by lowering initial 

capital requirements and accelerating return on investment.

The secondary way in which utilities acquire RECs and SRECS is by purchasing them on the 

open market on an as-needed basis. While it is up to buyers and sellers of RECs to come together 

on their own, the state has organized a formal exchange to facilitate SREC trading. Through the 

North Carolina SREC exchange, the state aims to promote a healthy market for the smaller 

SREC market by creating liquidity and providing transparency to participants.

4.2.4 Market Expansion

Having initially focused on promoting renewable energy through market preparation and market 

creation policies, the state legislature then turned its attention to market expansion policies. In 

2008, the state legislature created a property tax exemption for PV systems that is equal to 80% 

of the system’s appraised value. In addition, residential systems that are not used to generate 

income or in connection with a business may be entirely exempt from property taxes as 

nonbusiness personal property. In 2009, the legislature authorized cities and counties to establish 
20 

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revolving loan programs to finance renewable energy and energy efficiency projects (House Bill

1389). In addition to these financial incentives, North Carolina has a 35% personal and corporate 

tax credit for renewable energy installations that has been in place since 1977 (NC Gen. Stat. § 

105-129.15 et seq.). These types of tax policies can stimulate market expansion by reducing the 

cost of installing a system through indirect means, such as avoided property taxes. This makes 

systems more affordable for developers and draws new participants into the market. 

Figure 7. Total installed PV capacity with policy timeline, North Carolina 2006-2012

4.2.5 Summary

The case of North Carolina is an example of how comprehensive policy can stimulate market 

adoption in an environment where non-policy factors are expected to be unfavorable to market 

development, but public support is strong. The rooftop rich context grouping comprises states 

with less favorable non-policy characteristics, but with an abundance of PV technical potential

within the group, North Carolina generally ranks in the middle in terms of electricity cost, 

income, and technical potential. However, North Carolina residents have demonstrated a stronger 

interest in energy-related policy than most in their group. In addition, they are one of four states 

within the rooftop rich group that has adopted an RPS, which has been shown to be a critical 

policy determinant of PV adoption (see Section 3). North Carolina’s first implementation of an 

RPS was more comprehensive than most of its peers’ RPSs. It included provisions for 

municipalities and electric cooperatives, as well as a solar set-aside. Therefore, while the state’s 

RPS is relatively young, it is characteristic of more mature policies. This is particularly relevant 

as it relates to the solar set-aside. 

In addition to having a comprehensive RPS, North Carolina has stimulated PV adoption through 

other types of policies, including market preparation- and market expansion-type policies. 
21 

This report is available at no cost from the National Renewable Energy Laboratory (NREL) at www.nrel.gov/publications.

Having adopted best practices for interconnection, the state allows energy producers to access 

the grid without unnecessary burdens. To expand the renewable energy market, North Carolina 

offers a variety of financial incentives that aim to reduce economic barriers. The state has also 

taken steps to ensure that there are multiple markets for SREC transactions. North Carolina is an 

example of the effectiveness of using a combination of market preparation, market creation, and 

market expansion policies. 

4.3 Motivated Buyer Group: Delaware

This case history focuses on the motivated buyer group and the policy history of Delaware. The 

motivated buyer group comprises states that have demonstrated public support for clean energy 

policy in the past or have economic conditions that favor PV adoption (i.e., above-average 

income and cost of electricity). States in this group are expected to have greater interest in PV 

technology based on the presence of one or both of these characteristics. These criteria resulted 

in a grouping of states with significant disparity in level of PV adoption. Five of the nine states 

had installed PV capacity per capita greater than the group average, while each of the remaining 

four states had installed capacity that was less than one-third of the group average.

4.3.1 State Profile: Delaware

Delaware’s non-policy characteristics suggest it should be among the bottom of its peer group 

for installed PV capacity. Compared to its peers in the motivated buyer group, it has the lowest 

ACEEE Energy Efficiency Scorecard score, below average income, below average cost of 

electricity, and the seventh lowest rooftop PV potential. Yet, Delaware has the highest installed 

PV capacity per capita in its group with 16.2 W/person—more than twice the motivated buyer 

group average. These characteristics are consistent with the finding (Section 3) that non-policy 

factors do not explain PV adoption within a highly motivated group of states.

Table 7. Quick Facts: Delaware

Delaware

(Rank) Group Average

ACEEE Scorecarda 18.5 (9/9) 30.1

Retail Electricity (¢/kWh)b 11.9 (6/9) 12.1

Median Incomec $55,269 (6/9) $57,123

PV Technical Potential (kW/capita)d 2,185 (7/9) 5,570

Installed Capacity (W/capita)e 16.2 (1/9) 7.1 a Foster et al. 2012

b Three-year average residential electricity (EIA 2010-2012) c Median household income (U.S. Census Bureau 2010) 

d Lopez et al. 2012

e Sherwood 2013

4.3.2 Market Creation

Net-metering rules in Delaware were first enacted as part of the Electric Utility Restructuring 

Act of 1999 (House Bill 10). As originally adopted, utilities were not required to offer net 

metering for systems over 25 kW in size, which limited potential renewable energy development 

to small- and medium-sized systems. Furthermore, credit for NEG could only be carried forward 

for 12 months, after which, it was forfeited to the utility without compensation to the producer. 
22 

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This policy effectively eliminated all economic incentives for renewable energy producers to 

develop systems with capacity beyond their own consumption needs. 

In an effort to create a larger market for renewable energy, the Delaware state legislature adopted 

an RPS as part of the Renewable Energy Portfolio Standards Act (REPSA) (Senate Bill 74) in 

2005. The act required IOUs and municipalities to procure 10% of total retail sales from 

renewable energy sources by the 2019 compliance year. In addition, the legislature sought to 

encourage the development of specific renewable resources through the use of multipliers. As 

such, electricity generated from solar energy is eligible for a 300% multiplier that applies to the 

general renewable energy portion of the RPS if the system is installed before Dec. 31, 2014, and 

meets certain requirements. In 2007, the legislature took further action to expand the market for 

renewable energy by doubling the RPS requirement to 20% and adding a 2.005% set-aside

specifically for solar (Senate Bill 19). The creation of a solar set-aside was a significant event

because it is the most meaningful determinant of installed PV capacity across all the policy and 

non-policy factors analyzed (Section 3). In 2010, Delaware revised the RPS to its present 

standards by increasing the renewable energy requirement to 25%, with a 3.5% solar set-aside by 

2025 (Senate Bill 119). 

To track compliance with RPS requirements, the Delaware PSC established a REC program with 

SRECs specifically for electricity generated from solar energy. These credits belonged to the 

energy producer, with the exception of NEG, which was forfeited to the utility. While the 

forfeiture was suboptimal from the owner perspective, facility owners retained control over the 

majority of their RECs that were fully transferable under REPSA. This created additional value 

for renewable energy producers, as utilities would purchase RECs and SRECs as a means to 

satisfy RPS and solar set-aside obligations without having to develop their own renewable 

energy facilities. As a result, additional markets have formed to facilitate the acquisition of 

SRECs by utilities. Initially, all SRECs were traded on the open market through organized 

exchanges. However, in an effort to stabilize prices through long-term contracts, the PSC has 

begun authorizing procurement programs, whereby renewable energy producers may respond 

directly to open solicitations from utilities seeking to enter into long-term contracts to purchase 

the SRECs that their systems produce. 

If a utility does not fulfill its RPS obligations in a given year through either its own production or 

by purchasing credits, it may compensate for the deficit through alternative compliance 

payments (ACPs). ACP programs create a ceiling on potential regulatory costs through a 

predetermined price schedule. Delaware’s ACP program becomes increasingly expensive each 

year that a utility is out of compliance. While this provides an escalating incentive to satisfy RPS 

obligations, utilities benefit from being able to anticipate the cost of noncompliance. 

Furthermore, ACP programs provide a basis to price RECs and SRECs trading in the open 

market. For example, under the current schedule, the cost of an ACP to meet the general 

renewable energy requirements of the RPS starts at $25 per MWh, whereas a solar ACP to 

satisfy the solar set-aside starts at $400 per MWh. By placing a premium on solar ACPs, utilities 

have a meaningful financial incentive to comply with the solar set-aside. 

In 2007, the legislature addressed the issue of net-metering limitations by requiring utilities to 

offer net metering to nonresidential customers with systems up to 2 MW in size for IOUs and 

500 kW for cooperatives (Senate Bill 8). By doing so, lawmakers expanded the potential market 
23 

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for renewable energy development. In 2009, the legislature improved the economics of net 

metering for PV system owners by requiring utilities to offer annual payments for NEG and by 

reassigning ownership of the associated RECs to the system owner (Senate Bill 85). 

Furthermore, Senate Bill 85 also increased the aggregate capacity of net metering that utilities 

had to offer from a maximum of 1% of monthly peak demand to 5%. 

4.3.3 Market Preparation

Even though the PSC instructed utilities to adopt interconnection rules based upon Interstate 

Renewable Energy Council (IREC) best practices in 2007 (Senate Bill 8), the state routinely 

received failing grades from Freeing the Grid (FTG 2013).7 Recognizing the need for 

improvement, the PSC determined it was necessary to issue specific rules for interconnection to 

standardize practices across utilities. Rather than promulgating entirely new rules, the PSC chose 

to use Maryland’s interconnection standards, which were based upon IREC standards, as a 

template (DSIRE 2013). Since adopting the revised rules, Delaware has received an “A” from 

Freeing the Grid for three consecutive years (FTG 2013). The new interconnection procedures 

include solar-friendly features, such as a fast track process for smaller systems (26 Del. C. § 

1014). 

4.3.4 Market Expansion

In addition to market creation and market preparation policies, Delaware has used market 

expansion policies to stimulate adoption of PV technology. In 2010, the state legislature lowered 

economic barriers to PV development by allowing additional ownership structures for energy 

generating facilities (Senate Bill 267). Third-party ownership structures can lower economic 

barriers by reducing or eliminating initial capital requirements for individuals. Furthermore, 

third-party ownership is a way communities that lack suitable space for PV to benefit from 

renewable energy. 

A number of state and utility rebate programs also support market expansion in Delaware. Utility 

customers throughout the state are eligible to receive rebates on PV systems based on installed 

capacity (DSIRE 2013). The rebates are funded under the authority of the Electric Utility 

Restructuring Act of 1999 and REPSA. Rebates lower economic barriers to PV development by 

reducing total system cost. Furthermore, because the rebates are paid up front, initial capital 

requirements are less burdensome. 

 

7 Freeing the Grid is a policy guide that evaluates the net metering and interconnection practices of every state. It is 

a collaborative effort of several nonprofit organizations. For more information, see http://freeingthegrid.org/. 
24 

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Figure 8. Total installed PV capacity with policy timeline, Delaware 1999-2012

4.3.5 Summary

The case of Delaware is an example of how policy can be effective in stimulating market 

adoption in an environment where non-policy determinants have demonstrated little or no 

influence. The motivated buyer group is characterized by states with higher expected interest in 

energy-related policy, higher cost of electricity, higher income, but less abundance of solar 

resources. Delaware, which ranks near the bottom of its group in all of these non-policy 

categories, has the highest installed capacity per capita of its peers. Delaware was able to 

accomplish this by enacting a variety of policy types that included market preparation, market 

creation, and market expansion. Chief among these was the adoption of a solar carve-out within 

the state’s RPS. Solar carve-outs have been shown to be the most meaningful determinant of 

installed PV capacity across states of all the factors they analyzed (see Section 3). At four years 

old, Delaware’s set-aside is the most mature in the group. However, it was not by market 

creation policy alone that Delaware emerged as a leader among its peers. In conjunction with the 

solar set-aside, the state prepared the market by adopting best practices for interconnection. This 

made it possible for renewable energy producers to access the grid and sell their SRECs in the 

open market or directly to utilities through procurement programs facilitated by the PSC. The 

legislature also worked to expand the PV market by lowering economic barriers through 

financial incentives. Delaware’s experience is an example of how comprehensive policy can be 

an effective tool to stimulate market adoption of PV in states where non-policy factors are 

favorable, but have not driven installed capacity.

4.4 Mixed Group: New Mexico

This case study focuses on the mixed group and the policy history of New Mexico. The mixed 

group is the largest of the state peer groups with seventeen members and comprises states with a 
25 

This report is available at no cost from the National Renewable Energy Laboratory (NREL) at www.nrel.gov/publications.

variety of non-policy factors influencing installed PV capacity, none of which suggests a singular 

motivation for adopting PV as it might in other peer groups. States in this group have a less 

robust history of enacting clean energy policy than the national average, sub-optimal economic 

conditions to stimulate PV adoption, and a range of technical potential for rooftop PV.8 Based on 

these non-policy characteristics, states in the mixed group are expected to demonstrate lower 

market penetration relative to the other three peer groups absent of policy.

4.4.1 State Profile: New Mexico

The state of New Mexico represents an opportunity to study the influence of policy on PV 

adoption because its non-policy characteristics suggest it should rank low in installed capacity 

within its peer group; yet, it is third out of 17. New Mexico’s installed capacity is nearly three 

times the group average, while only possessing two-thirds of the average rooftop PV potential 

and below average household income. However, New Mexico also exhibits one non-policy 

characteristic that would suggest a higher propensity for PV technology relative to its peers. The 

state has a robust history of supporting clean energy policy, ranking fourth in the group in terms 

of the ACEEE Energy Efficiency score. Economic conditions are mixed with an approximately 

20% higher average cost of residential energy, but below average median household income. 

4.4.2 Market Creation

In 1999, the New Mexico state legislature took its first step toward creating a market for 

renewable energy by instructing the PRC to evaluate the merits of adopting an RPS (Electric 

Utility Industry Restructuring Act [Restructuring Act] Senate Bill 428). The PRC concluded that 

an RPS was desirable and promulgated Rule 591 (NMAC 17.9.591), which established an RPS 

for IOUs that would go into effect with deregulation under the Restructuring Act. However, as 

deregulation neared, the legislature grew concerned regarding difficulties that other states were 

having with deregulation and decided in 2001 to delay implementation of the Restructuring Act. 

With the act facing delays and possible repeal, the PRC promulgated Rule 573 (NMAC 

17.9.573), which implemented the RPS as an independent measure. Rule 573 also established a 

REC program to document RPS compliance. Furthermore, by allowing utilities to purchase 

RECs to meet RPS obligations, the PRC created financial value for distributed generation (DG) 

facility owners by creating a market for the RECs they produced and allowed utilities the 

flexibility to acquire RECs rather than invest in renewable energy facilities. Rule 573 went into 

effect in July 2003 and was codified into state law through the Renewable Energy Act (Senate 

Bill 43) the following year. 

 

8

Technical potential for rooftop PV is not simply a measure of available solar resources in a state. It is also a 

function of existing constraints to PV development, such as rooftop space and land-use restrictions.
26 

This report is available at no cost from the National Renewable Energy Laboratory (NREL) at www.nrel.gov/publications.

Table 8. Quick Facts: Mixed Group

New Mexico

(Rank)

Group Average

ACEEE Scorecarda 18.5 (4/17) 12.5

Retail Electricity (c/kWh)b 11.0 (4/17) 9.8

Median Incomec $45,098 (11/17) $47,198

PV Technical Potential (kW/capita)d 6,513 (8/17) 9,894

Installed Capacity (W/capita)e 9.2 (3/17) 3.1 a Foster et al. 2012

b Three-year average residential electricity (EIA 2010-2012) c Median household income (U.S. Census Bureau 2010)

d Lopez et al. 2012 e Sherwood 2013

As the Renewable Energy Act went into effect in 2004, Governor Bill Richardson issued 

Executive Order 2004-019, declaring his intent to make New Mexico the “Clean Energy” state 

and creating a cabinet-level position to oversee the initiative. This was consistent with Governor 

Richardson’s 2002 campaign platform for economic development through leadership in 

renewable energy. Over the next several years, the state legislature supported this initiative by 

passing numerous clean energy laws, which are now the framework for New Mexico’s current 

renewable energy policy.

Senate Bill 418, which amended the Renewable Energy Act in 2007, was the most significant 

legislation enacted during this period. While the amendments expanded the scope of the RPS to 

include rural electric cooperatives and created greater RPS requirements for IOUs to achieve by 

2015 and 2020, the real impact in terms of distributed PV was that it caused the PRC to review 

its portfolio diversification rules. As originally enacted Rule 572 (formerly Rule 573), renewable 

energy producers received a different number of RECs depending on the resource used to 

generate the electricity. Indeed, this reflected the legislature’s intent of creating a diversified 

energy portfolio. After four years, however, wind accounted for 99% of the renewable energy 

requirement (Final Order Case 07-00157-UT). The PRC considered this a failure in terms of 

creating a diversified energy portfolio. It therefore instituted specific portfolio diversification 

requirements (set-asides) in 2008 that included a 20% allocation to solar technology (NMAC 

17.9.572). Furthermore, the revised rules established a new set-aside specifically for DG. IOUs 

would be required to procure no less than 1.5% of energy sold from DG by 2011, increasing to 

3% by 2015. 

4.4.3 Market Preparation

In an effort to remove unnecessary obstacles to the DG set-aside of the state’s RPS, 

policymakers enacted a series of market preparation policies. In 2007, the state legislature 

strengthened solar access laws by amending the Solar Rights Act to preclude all restrictions on 

the placement of solar collectors, except in historic districts (Senate Bill 1031). They also 

adopted the Solar Collector Standards Act (House Bill 610), which modified the state’s 

residential building code to require that all new construction include PV-ready roofs.

In 2008, the PRC addressed interconnection and net-metering challenges by promulgating rules 

specifically for small energy producers with installations no larger than 10 kW. The new and 
27 

This report is available at no cost from the National Renewable Energy Laboratory (NREL) at www.nrel.gov/publications.

revised rules (Rule 568, Rule 569, and Rule 570) simplified utility interconnection standards. 

Krasko and Doris 2012 found that best practices for DG-compliant net metering and 

interconnection standards reduce system costs by effectively allowing customers to store excess 

generation.

4.4.4 Market Expansion

New Mexico has also implemented a number of complementary market expansion policies. In 

2006, the state enacted the Solar Development Tax Credit (SDTC), which allowed for a 30% 

investment tax credit (ITC) up to $9,000. When Congress renewed the federal ITC and lifted the 

$2,000 federal limit in 2008, the state legislature revised the SDTC to be a 10% credit that was 

independent of the ITC. PV systems are also exempt from property taxes until the property is 

sold. Finally, to meet the state’s DG set-aside requirements of the RPS, utilities have enacted 

programs to purchase RECs from customers with PV systems. In New Mexico, REC payments 

are based on estimates related to system size and paid to the customer once the system is put into 

service. This allows customers to recover a portion of initial system costs immediately (as 

opposed to REC payments paid over time based on actual production). REC procurement 

programs such as these are only possible when DG system owners have ownership of the RECs 

they generate and utilities are allowed to purchase RECs to fulfill RPC obligations.

Figure 9. Total installed PV capacity with policy timeline, New Mexico 2003-2012

4.4.5 Summary

The state of New Mexico is an example of the importance of policy diversity and strategic 

implementation. While the first solar access laws were enacted in 1977, initial market 

preparation policies alone were insufficient to facilitate broad adoption of PV technology. 

Moreover, policy diversity is not enough to achieve market penetration goals. It is also important 
28 

This report is available at no cost from the National Renewable Energy Laboratory (NREL) at www.nrel.gov/publications.

to develop effective policies that are consistent with best practices. The Solar Rights Act serves 

as an example. While it established solar energy as a property right, it did not initially protect 

system owners from local ordinances or restrictions, thereby reducing the effectiveness of the 

policy. 

The overarching political environment in New Mexico had an impact on the development of 

policy. The bulk of activity occurred at a time when there was significant public support for the 

environmental agenda in the state capital. In 2002, the people of New Mexico elected Governor 

Bill Richardson on a platform advocating for the state to become a leader in renewable energy. 

The legislature demonstrated its support for this goal by repeatedly enacting and amending state 

laws to further this aim. The PRC promulgated rules to execute the legislature’s intent and 

actively revised regulations they found to be ineffective. As a result, New Mexico developed a 

comprehensive framework for renewable energy policy and emerged as a leader among its peers 

in installed PV capacity.
29 

This report is available at no cost from the National Renewable Energy Laboratory (NREL) at www.nrel.gov/publications.

5 Conclusion

The purpose of this research is to better understand the role of state policy in the effective 

development of behind-the-meter PV markets. In order to do so, a two-pronged approach was taken. 

First, a grouping and analysis of the states based on non-policy context was developed to increase 

understanding of policy impact in different situations. Second, case histories were developed to 

augment the quantitative analytics through richer context to the policy development processes 

within each state grouping. The result is the beginning of actionable strategies for states that 

understand their own non-policy context and are interested in developing comprehensive support 

systems for the development of solar PV markets that emphasize private sector development and 

short- to medium-term government intervention. Results and observations from the quantitative 

analysis and case histories are detailed in the body of the report, with major findings being: 

• Solar-related policy has a quantified effect on solar markets (as measured by installed 

capacity) 

• Non-policy factors influence the effectiveness of those policies in driving solar markets in 

situations when the non-policy factor is extreme. For example, if costs are very low for 

competing electricity, market-enabling policy, such as interconnection and net metering, 

influence is weakened due to increased economic hurdles to overcome. 

• Solar set-aside is most consistently correlated with increased installed capacity. This 

relationship spans across all groups. For example, the correlation is particularly strong in 

states with less supportive non-policy market factors. This is borne out in the mixed group 

of states, which has a lackluster non-policy-driven market for solar, and the findings indicate 

the impact of solar set-asides is particularly strong in this group. 

• Having a suite of policies in place that is consistent with best practices is correlated with 

increased solar PV installations. 

• Based on the case history findings, there appear to be effective pathways specific to the nonpolicy contexts of the state in question: 

o Expected leaders. In Maryland, a comprehensive policy portfolio, with equal 

emphasis on all policy types is driving recent market development. 

o Rooftop rich. In North Carolina, strong interest from the populous in clean energy 

related policy distinguishes it from other members of the group. 

o Motivated buyers. The experience in the state of Delaware illustrates that targeted 

market preparation and market creation policy can effectively stimulate PV 

development. 

o Mixed. In New Mexico, the leading state for installed capacity in the group, policy 

diversity and strategic implementation have proven to be critical in effectively 

supporting the market. 

• Findings from the quantitative analysis and the case histories indicate there may be a 

minimum threshold of policy scope and quality that is necessary to spur solar PV markets. If 

confirmed and expanded, this finding could provide insights into the most effective policy 

development strategies for each state context and provide actionable strategies for individual 

states. This is the primary focus of follow-on work to this research. 
30 

This report is available at no cost from the National Renewable Energy Laboratory (NREL) at www.nrel.gov/publications.

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Appendix: Alternative Grouping Strategies Based on 

Non-Policy Contextual Factors

Introduction

A wide variety of policy and non-policy factors affect the development of solar photovoltaic 

(PV) markets in the United States. There is a broad and growing body of literature on how 

national and subnational policy factors influence development (including Krasko and Doris 

2013; Carley 2009). Study of non-policy factors is typically done within these studies as a 

background measurement, normalized through energy price and/or per capita income (including 

Sarzynski et al 2012). The research presented here attempts to describe these non-policy factors 

with more nuance and detail. Understanding the complexities of these factors and their 

interaction allows for greater depth of study in how different policies and policy designs can 

impact the development of PV markets in different types of jurisdictions. A densely populated 

urban area with high electricity prices and relatively few detached one-family households, for 

example, may be more suitable for certain policy tools than a more suburban jurisdiction with 

low electricity prices, which has its own optimal policy pathway.

Previous literature on the subject provides some guidance in identifying important non-policy 

factors that influence the decision to adopt solar PV technology. Zahran et al (2008) used countylevel, cross-section regressions to predict whether a county had any homes heated with solar 

energy (logistic) and the number of homes heated with solar energy (negative binomial). They 

found evidence that the number of housing units, solar radiation, the square of the maximum 

temperature, median home value, urbanization, and political leanings measured by percentage 

voting democrat in the last presidential election and participation in the International Council for 

Local Environmental Initiatives were all significant factors in the logistic regression predicting 

whether or not a county had any households heated with solar energy. 

Carley (2009) used a fixed effects vector decomposition model on state-level panel data to 

predict the logged proportion and the total amount of the share of renewable energy in a state’s 

annual electricity generation. Significant factors in predicting a state’s logged share of renewable 

energy generation included solar potential, average retail electricity price, electricity use per 

capita, gross state product per capita, natural resource employees per capita, percent petrol/coal 

manufacturing of total gross state product, House League of Conservation Voters’ environmental 

scorecard, and several other mostly policy-related factors.

Doris and Gelman (2011) used regression analysis to explain between 40% and 60% of the 

variations between state renewable energy market growth through policy factors, including 

mandates and incentives. Significant non-policy factors in that analysis included population and 

electricity price. The work also found evidence that differing combinations of policies, which

varied by location, could be correlated with different market penetration levels. 

Sarzynski et al (2012) used a cross-sectional time-series regression on state-level data to predict 

the amount of grid-tied photovoltaic capacity installed in a state. Although the focus was on 

policy-related factors, there were some non-policy control variables that were found to be 

significant, including the logged average commercial electricity price and the logged gross 

domestic product per capita in a state. 
34 

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Although there are numerous non-policy factors that are significant with respect to several 

measures of solar market penetration, many of these are closely related and in practice could 

likely be combined, resulting in a small number of variables. This analysis take two different 

approaches—one involving a comprehensive set of significant factors and the other a more 

limited set, focusing only on the most critical variables.

To date, researchers have tried to normalize for non-policy factors to focus on the effects of 

policy. This approach naturally leads to a “one size fits all” view of policy and could overlook 

important relationships between non-policy factors and the effectiveness of various policy tools. 

In order to gain an insight into these relationships, the states were grouped and clustered with 

respect to the significant non-policy factors that describe the context of their customer-sited solar 

PV market. 

The strategy of this analysis was to first identify the likely factors that drive solar development 

outside of policy intervention using two different methods, and then develop state groupings 

based on those two different strategies. This paper follows a similar structure. The discussion 

section provides a comparison of the differences between groupings that result from the 

inclusion of different factors. 

Identifying Significant Non-Policy Factors

This research took two different approaches in identifying important factors for consideration. 

The first approach was to test the significance of various non-policy factors in a household’s 

decision to adopt solar PV technology using a household-level logistic regression. The second 

approach was a list culled from existing literature that aimed to combine related factors and 

capture only the most critical variables. 

Comprehensive Factors: Household-Level Logistic Regression

In this area of the analysis, a household-level logistic regression was used to find significant nonpolicy factors that help explain whether a household is heated with solar energy or not. The data 

set used was the 2007-2011 5-year American Community Survey (ACS) Public Microdata 

Sample (PUMS). The ACS is a statistical survey that samples a small percentage of the 

population every year and asks various questions pertaining to the household and the people 

living there. 

The PUMS data contains individual survey responses, rather than aggregate data for a 

geographic region, and therefore allows one to analyze individual household decisions directly. 

The full data set contains 6,999,047 housing observations corresponding to a single household 

each, 15,199,756 population observations corresponding to a single person each, and a number 

of variables. A full data dictionary with all of the variable definitions is available from the 

Census Bureau (Census Bureau 2013). Unless otherwise stated, all variables are defined as in the 

data dictionary. Due to the size of the data, this analysis was done using the R programming 

language along with ff, ffbase, and biglm packages. 
35 

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Dependent Variable

The most pertinent ACS question asks “which fuel is used MOST for heating this house, 

apartment, or mobile home?” and allows respondents to answer by completing check boxes.9

The dependent variable, SOLAR, is equal to one if the respondent answers “solar energy” and 

zero otherwise. Although this question does not distinguish between passive and active solar 

heating systems, the word “fuel” implies an external source of energy, and similar to previous 

research (Zahran et al. 2008), it is assumed this generally refers to active PV systems.

Data Preparation

The original data set contains a number of household observations that do not have an answer to 

the question about house heating fuel; they were dropped from this analysis. More than half of 

these are vacant lots, while the rest are group quarters, such as correctional facilities, military 

barracks, and student housing. 

Most household observations are connected to multiple population observations because multiple 

people live there. For the purpose of this analysis, however, all but the head of the household was

dropped, which was assumed to be the first person listed on the ACS survey response. 

Households, rather than individual people, were of interest because the decision to install 

customer-sited PV is presumably done at the household level. This also avoided the complication 

of having multiple values for population variables associated with a single household observation 

by only considering one influential person in the household. 

After merging the remaining housing records with the heads of the households from the 

population records (matched by household serial number), 5,960,233 observations were left, 

which were associated with both housing and population variables. The dependent variable was

very sparse, with only 2,455 households in the sample heating their home with mostly solar 

energy. 

Property Characteristics

As customer-sited solar PV installations are specific to a place of residence, the characteristics of 

the property are thought to influence the decision to adopt customer-sited PV technology. The 

most basic of these is property type, or whether or not the property is a single-family detached 

building, denoted by the binary variable HOU. This was included because installing customersited PV on other property types, such as apartment homes, mobile homes, and duplexes, may 

present extra challenges above what a single-family detached house would. 

Another related property characteristic is ownership status, represented by the variable RENT, 

which is equal to one if the property is a rental unit and zero otherwise. Ownership status is 

thought to influence the decision to install customer-sited PV because an investment into this 

technology has a payback period measured in years, and it would be more difficult to make such 

an investment on a property one is renting. 

 

9 Possible answers are “Gas: from underground pipes serving the neighborhood,” “Gas: bottled, tank, or LP,” 

“Electricity,” “Fuel oil, kerosene, etc.,” “Coal or coke,” “Wood,” “Solar energy,” “Other fuel,” or “No fuel used.”
36 

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The variable YBL denotes the time period the house was built. This variable is coded between 

one and nine, as defined in the Census data dictionary, where a value of one corresponds to a 

property that was built in 2005 or later and larger integers denote earlier time periods. It is 

hypothesized that installing PV on a newer home is easier because a newer home may be more 

PV ready and the investment may make more sense. In addition, newer homes do not face the 

potential difficulties of installing PV in historic neighborhoods. 

To account for the popularity and acceptance of solar technology in the area, a peer effects 

variable, denoted PEER, was included. This is equal to the percentage of household observations 

in the same Public Use Microdata Area (PUMA) that reported heating homes with solar energy. 

The Census Bureau defines a PUMA as an area with a population of 100,000 or more that can 

vary in size.

It is also possible the size of the property has some effect on whether or not a household is 

heated with solar energy. To test this, BDS (the number of bedrooms), was included. 

Resident Characteristics

Previous studies found that gross state product per capita had an effect on PV capacity 

(Sarzynski et al 2012) and renewable energy generation (Carley 2009) in states. The income 

factor was accounted for by including HINCP, the reported total household income in the past 

year.

10 These values were all converted to 2011 dollars using weighting factors found in the data 

set.

To get a measure of the average electricity cost in an area, a new variable, AVGELEP, was

generated, which is equal to the average reported monthly electricity cost for households in the 

PUMA that reported paying for electricity and did not report heating with solar energy. Before 

averaging, the ELEP values were also converted to 2011 dollars using weights found in the data 

set.

Head of Household Characteristics

Zahran et al. (2008) found some evidence the proportion of residents aged 40 to 49 had an effect 

on the number of households in a county using solar energy for heating. The age of the head of 

the household, AGEP, and its square, AGEP,

2 were included to test for a nonlinear relationship 

between the age of the head of the household and whether the home is heated with solar energy. 

Educational attainment is hypothesized to affect the decision to adopt PV in a similar way so 

SCHL was recoded to create COLL, which is equal to one if the head of the household has a 

bachelor's degree and zero otherwise, and GRAD, which is equal to one if the head of the 

household has a graduate degree and zero otherwise. 

There is some evidence that household solar energy use is more prevalent in more urbanized 

areas (Zahran et al. 2008). It is important to account for this factor, but it is not immediately 

obvious how to measure it at the household level. The variable TR_WALK was included, which 

is equal to one if the head of the household reportedly walks to work and zero otherwise. While 

 

10 The units on this variable are rescaled from dollars to thousands of dollars in the regression.
37 

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biking or using public transport to get to work can be seen as an indication of energy saving 

behavior, it is assumed being able to walk to work is more of an indication of living in an 

urbanized area. It is also hypothesized certain behavior pertaining to energy use may be 

correlated with the decision to adopt solar PV technology, so TR_BIKE and TR_PUB were also 

included. These variables were recoded from JWTR in the data set and equal to one if the head of 

the household reportedly bikes or takes public transport to get to work, respectively, and zero 

otherwise.

Results

The regression results are presented in Table A-1. Yearly effects denote the year the household 

was interviewed and data was gathered, and are relative to 2007. State-fixed effects were used to 

account for various other factors, including state policy and solar resource, and are relative to 

California. There is no variable for District of Columbia because there are no solar heated 

households for DC found in this data set. 

Table A-1. Logistic Regression Resultsa 

Variable Coefficient Std. Error P-Value

(Constant) -9.1632 0.3444 0.0000***

HOU 0.6352 0.0599 0.0000***

BDS -0.2033 0.0243 0.0000***

YBL -0.1237 0.0097 0.0000***

RENT -0.1207 0.0615 0.0496*

AVGELEP 0.0060 0.0019 0.0015**

PEER 0.2331 0.1153 0.0431*

HINCP 0.0006 0.0002 0.0071**

AGEP 0.0636 0.0083 0.0000***

AGEP2 -0.0005 0.0001 0.0000***

COLL 0.1934 0.0528 0.0002***

GRAD 0.3347 0.0628 0.0000***

TR_WALK 0.4074 0.1424 0.0042**

TR_BIKE 0.8759 0.2124 0.0000***

TR_PUB 0.3340 0.1235 0.0069**

Year 2008 0.0279 0.0684 0.6828

Year 2009 0.0687 0.0675 0.3092

Year 2010 0.2013 0.0653 0.0021**

Year 2011 0.3129 0.0639 0.0000***

AL -2.3844 0.3372 0.0000***

AK -2.5721 1.0014 0.0102*

AZ -0.3080 0.1138 0.0068**

AR -1.5941 0.2930 0.0000***

CO 0.4307 0.0934 0.0000***

CT -1.2448 0.2345 0.0000***

DE -1.2495 0.4504 0.0055**

FL -1.3168 0.1116 0.0000***

GA -2.2190 0.2246 0.0000***

HI 2.7472 0.0851 0.0000***

ID -0.6709 0.2630 0.0108*

IL -1.7213 0.1664 0.0000***

IN -1.5076 0.1989 0.0000***

IA -1.3096 0.2630 0.0000***

KS -1.3923 0.2816 0.0000***

KY -1.6551 0.2546 0.0000***

LA -1.9311 0.2927 0.0000***
38 

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Variable Coefficient Std. Error P-Value

ME -0.7932 0.2929 0.0068**

MD -1.9019 0.2554 0.0000***

MA -0.9859 0.1594 0.0000***

MI -1.4772 0.1602 0.0000***

MN -1.3473 0.2018 0.0000***

MS -2.6594 0.5025 0.0000***

MO -1.8168 0.2346 0.0000***

MT -0.8712 0.3570 0.0147*

NE -0.9194 0.2815 0.0011**

NV -0.7576 0.1998 0.0001***

NH -1.0986 0.3369 0.0011**

NJ -1.7402 0.1988 0.0000***

NM 1.0497 0.1067 0.0000***

NY -1.1696 0.1152 0.0000***

NC -1.3459 0.1483 0.0000***

ND -1.8129 0.7089 0.0105*

OH -1.3714 0.1450 0.0000***

OK -2.8808 0.5023 0.0000***

OR -0.7059 0.1705 0.0000***

PA -1.2370 0.1364 0.0000***

RI -1.5223 0.5023 0.0024**

SC -2.1579 0.3057 0.0000***

SD -0.8820 0.4113 0.0320*

TN -2.3741 0.2929 0.0000***

TX -2.3329 0.1594 0.0000***

UT -0.9033 0.2430 0.0002***

VT -0.6186 0.3814 0.1048

VA -1.8261 0.2037 0.0000***

WA -1.0827 0.1559 0.0000***

WV -0.8445 0.2630 0.0013**

WI -1.0865 0.1696 0.0000***

WY -0.1818 0.3371 0.5897 a Observations: 5,960,233 

* p < 0.05

** p < 0.01 

*** p < 0.001

The regression results indicated a detached one-family household is more likely to heat their 

home with solar energy. Comparing the units in structure variable between solar heated 

households and all households in Figure A-1, solar heated households are more likely to be 

detached one-family units.
39 

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Figure A-1. Units in structure—all households compared to households heated with solar energy

Household age, represented by YBL, was found to be significant with newer properties being 

more likely to be heated with solar energy. Comparing the values of YBL between solar heated 

households and all households in Figure A-2, solar heated households tend to be newer. There 

are also more solar heated homes built in the 1980s, likely because of the response to the 

previous decade’s oil crisis and increased concern over energy security at the time. 

Figure A-2. When structure first built—all households compared to solar heated households

4.06%

2.67%

3.18%

3.58%

3.53%

3.02%

0.09%

6.26%

5.55%

68.05%

5.09%

2.53%

2.04%

2.08%

1.96%

1.63%

0.29%

2.36%

6.31%

75.72%

50 or more Apartments

20-49 Apartments

10-19 Apartments

5-9 Apartments

3-4 Apartments

2 Apartments

Boat, RV, van, etc.

Mobile home or trailer

One-family house attached

One-family house detached

Units in Housing Structure

Solar Heated Households All Households

14.20%

5.83%

11.72%

11.47%

16.17%

13.62%

13.80%

7.92%

5.28%

9.90%

3.87%

7.66%

9.78%

16.13%

20.86%

13.44%

7.86%

10.51%

1939 or earlier

1940 to 1949

1950 to 1959

1960 to 1969

1970 to 1979

1980 to 1989

1990 to 1999

2000 to 2004

2005 or later

When Structure First Built

Solar Heated Households All Households
40 

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The variable RENT was found to be significant at the 5% level with a negative effect. This is 

somewhat expected given what is known about investment into customer-sited PV technology. 

Household income was found to be significant, with a positive effect on the probability of PV 

adoption. This was also expected, given what is known about the relative costs of PV 

technology. 

The average electricity cost in the area, AVGELEP, was significant and positive, which could 

mean higher electricity prices provide incentive to adopt solar technology. Peer effects were also 

found to be significant and positive, suggesting the acceptance and popularity of solar 

technology in the area likely influences a household’s decision to adopt solar. 

For demographic variables associated with the head of the household, age, college, and advanced 

degree were found to be significant. There was some evidence for a nonlinear relationship 

between age and PV adoption, but the maximum effect seemed to be for householders in their 

early 60s rather than their 40s, as suggested in Zahran et al. (2008). It is possible this regression 

suffered from omitted variable bias, so strong conclusions were avoided about the peak age for 

PV adoption; however, it is accepted age is a significant factor. The assumption the first person 

listed on the ACS is the “head” of the household may also have contributed to this result.

The method of transportation used by the head of the household to get to work was found to be 

significant for walking, biking, and public transport. These variables can be seen as both an 

indication of urbanization and an indication of energy conserving behavior, but here it was

assumed walking corresponds more to urbanization and biking corresponds more to general 

energy saving behavior, while public transport is a combination of both. 

Yearly effects, relative to 2007, were all positive and increasing but only the last two years were

statistically significant. This indicates that PV adoption is on the rise through time, likely due to 

increased public awareness, lower technology costs, and other factors. 

The state effects, relative to California, were statistically significant at the 5% level for 48 out of 

the 50 states. These were used to control for other omitted factors. Their significance indicates 

various state-level factors, most importantly state policy, play an important role in PV adoption. 

Limited Factors

While the regression above produced many significant factors, several of these are related or go 

hand in hand. A limited and manageable set of factors may be good enough to reflect reality in 

practice. The age and education variables, for example, are likely correlated with household 

income to some extent. When grouping the states according to similar market context for 

customer-sited PV, accounting for average ages and average levels of education may not be all 

that useful after accounting for household income. 

The limited set of factors consisted of median household income, average residential electricity 

price, energy efficiency score, and solar technical potential. Household income (Census Bureau 

2010) was included because of the relative costs of PV technology. Although this is less 

important when power purchase agreements are available, different measures of wealth generally 

correlate with solar or renewable energy capacity (Zahran et al. 2008; Carley 2009; Sarzynski et 

al. 2012). Electricity price (DOE 2013) was included because a higher price is expected to 
41 

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increase the rate of return on customer-sited PV, and has been found to be significant in previous 

literature (Carley 2009; Doris and Gelman 2011; Sarzynski et al. 2012). The state energy 

efficiency scores were included as a proxy measurement for each state’s interest in, and progress 

toward, addressing energy-related issues. They were found in Foster et al. (2012) and based on 

utility public benefits programs and policies (40%), transportation policies (18%), building 

energy codes (14%), combined heat and power (10%), and appliance and equipment efficiency 

standards (4%). Previous literature identified similar proxy variables to be significant, including 

participation in the International Council for Local Environmental Initiatives (Zahran et al. 

2008), and House League of Conservation Voters’ environmental scorecard (Carley 2009). The 

estimates for state technical potentials of rooftop PV were gathered from Lopez et al. (2012), and 

originally derived in Denholm and Margolis (2008). These estimates were based on solar 

resource and available flat roof area after accounting for shading, rooftop obstructions, and other 

constraints. Measures of solar resource have also been found to be significant in previous 

literature (Zahran et al. 2008, Carley 2009). 

Grouping States by Non-Policy Factors

In this section, different methods of grouping the states according to the similarity of their nonpolicy market contexts for solar PV are explored. 

Comprehensive Factors

In this area of the analysis, the states were grouped using a comprehensive set of factors 

informed by the logistic regression. The states were partitioned into their groups using k-means 

clustering. This method partitions data points into k distinct clusters based on the distances 

between the cluster centers. For this study’s purpose, a data point was a state, represented by a 

vector of variable values. Most of these were state aggregate versions of significant variables 

found in the regression. The one exception was electricity price, which was reported in terms of 

cents per kilowatt-hour (kWh) (EIA 2013) instead of monthly cost. Variables that define each 

state vector included the following:

• Percentage of households that are one detached structure (agHOU) 

• Percentage of households that were built in 2005 or later (agYBL) 

• Percentage of households that are rental units (agRENT) 

• Average household electricity price in cents/kWh (agELEC) from (EIA 2013)

• Average household income in thousands of dollars (agHINCP) 

• Percentage of heads of households with a college degree (agCOLL) 

• Percentage of heads of households with an advanced degree (agGRAD) 

• Percentage of heads of households that walk to work (agTRWALK) 

• Percentage of heads of households that bike to work (agTRBIKE) 

• Percentage of heads of households that take public transit to work (agTRPUB). 

There are various distance measures, normalization techniques, quality indices, and methods 

available for clustering. In this study, clustering was done using the clusterSim R package, which 
42 

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compares several of these and returns the best solution according to a chosen cluster quality 

index (Walesiak and Dudek 2013; Walesiak 2008). To choose k, the number of clusters, the rule 

of thumb can be used: k ≈ (n/2)0.5 (Mardia et al. 1979), and initially set, k = 5. A different 

number of clusters can potentially create more distinct groups, so k was allowed to vary between 

4 and 6. The clusterSim package returned a clustering found using the general distance measure 

(see Jajuga et al. 2003), normalization within range [-1, 1], and four clusters based on the 

Silhouette cluster quality index. A visual representation of the results, as mapped to two 

dimensions (Components 1 and 2), is presented in Figure A-3. The group averages are displayed 

in Table A-2. 

Figure A-3. State clustering
43 

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Table A-2. Group Averages with Comprehensive Grouping

Group 1 Group 2 Group 3 Group 4

California

Connecticut

Hawaii

Maryland

Massachusetts

New Jersey

New York

Alaska

Colorado

Delaware

Illinois

New Hampshire

Rhode Island

Virginia

Washington

Arizona

Florida

Georgia

Minnesota

Nevada

North Carolina

Oregon

Pennsylvania

Texas

Utah

Vermont

Wyoming

Alabama

Arkansas

Idaho

Indiana

Iowa

Kansas

Kentucky

Louisiana

Maine

Michigan

Mississippi

Missouri

Montana

Nebraska

New Mexico

North Dakota

Ohio

Oklahoma

South Carolina

South Dakota

Tennessee

West Virginia

Wisconsin

agHOU 59.8 67.6 70.1 75.4

agYBL 3.5 5.1 6.9 5.1

agRENT 29.8 24.9 24.1 21.4

agELEC 18.6 13.0 11.2 10.4

agHINCP 92.9 79.9 70.0 60.9

agCOLL 38.0 34.0 29.7 24.6

agGRAD 16.7 13.7 11.1 8.8

agTRWALK 1.9 2.2 1.6 1.6

agTRBIKE 0.3 0.4 0.4 0.2

agTRPUB 5.5 2.1 1.3 0.4
44 

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The most distinct group of states is made up of the leading markets for solar PV. Group 1 has by 

far the highest group average electricity price, the highest average household income, and the 

most educational attainment among the population. In terms of housing, this group of states has 

the smallest percentage of single-family detached homes, the largest percentage of rental units, 

and the smallest percentage of households built in 2005 and after. Despite the fact that its 

housing stock does not look favorable for customer-sited PV, this is a very motivated group in 

terms of PV technology adoption, and it includes some of the highest solar capacity states, 

including California, New Jersey, and Hawaii. This group does not stand out in terms of walking 

or biking to work, but it has the most developed public transportation, with over 5% of the 

observations (heads of households) taking public transit to work. 

On the other side of the spectrum, Group 4 has the lowest group average electricity price, the 

lowest household income, and the least educational attainment. The average percentages of the 

population either walking, biking, or taking public transit to work are also the lowest among the 

groups. Despite this, Group 4 has the highest percentage of single-family detached households 

and the lowest percentage of renters. While the housing stock does seem well-suited for 

customer-sited PV, the usual financial motivators are not present. 

Groups 2 and 3 are somewhere in between the two extremes. Group 2 is more urban, it has the 

highest percentage of observations that walk to work and the second highest for biking and 

public transit. Group 3 is the leader for biking to work and has the highest percentage of 

households built in 2005 or after. 

Limited Factors

With the limited set of factors, the groups were defined as follows: 

• Expected leaders have technical potentials for rooftop PV above the median value and 

energy efficiency scores above the average. Therefore, expected leaders have a lot of 

potential for customer-sited PV and are motivated to address energy-related issues. 

• Motivated buyers have above-average energy efficiency scores or both above-average 

electricity prices and income. This group is motivated to invest in customer-sited PV 

either because high electricity prices and incomes make it an attractive investment or 

because they are generally motivated to address energy-related issues. 

• The rooftop rich group has an above-average technical potential with below-average 

incomes, electricity prices, and energy efficiency scores. This group has the potential for 

a large amount of PV without having the usual motivators to take advantage of this 

potential. 

• The remaining states are mixed. First, the selected leaders were picked; second, the 

motivated buyers; third, the rooftop rich. The remaining states were assigned to the 

mixed group, creating the grouping in Table A-3. 

Hawaii and Alaska were excluded because the data for their technical potential for rooftop PV is 

not available. 
45 

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Table A-3. Group Averages with Limited Grouping

Expected Leaders Motivated Buyers Rooftop Rich Mixed

Arizona Connecticut Alabama Arkansas

California Delaware Georgia Florida

Colorado Iowa Indiana Idaho

Illinois Massachusetts Louisiana Kansas

Maryland New Hampshire Missouri Kentucky

Michigan Oregon North Carolina Maine

Minnesota Rhode Island Ohio Mississippi

New Jersey Utah Oklahoma Montana

New York Vermont South Carolina Nebraska

Pennsylvania Tennessee Nevada

Washington Texas New Mexico

Wisconsin North Dakota

South Dakota

Virginia

West Virginia

Wyoming

Tech. Potential 

(GWh/year) 26,866 5,570 25,274 10,356

Energy Efficiency 

Score

28.4 30.1 13.3 12.2

Household 

Income 

(thousands of 

dollars)

53.7 57.1 43.5 46.7

Electricity Price 

(cents/kWh) 11.8 12.1 10.2 9.7

Discussion

The two grouping strategies presented here have their own merits and produced somewhat 

different results. 

With the first strategy, a comprehensive, though admittedly not an exhaustive, set of non-policy 

factors using a household-level logistic regression was tested and used to inform variable 

selection for state clustering. Advantages of this method include being relatively certain that 

most important non-policy factors have been included and that the states have been grouped in 

the most logical way based on the data. The major disadvantage is factors that are related to each 

other are included as separate variables when clustering, which could give more weight to these 

demographic factors than warranted. After accounting for income and age, it may not be useful 

to include two measures of educational attainment on top of all that, for example.

The second strategy attempted to combine important, but related, non-policy factors together into 

a small number of variables and use measures or proxy values of these variables to group the 

states. One of the major advantages is that grouping is done based on broad, equally weighted

categories. Solar rooftop potential, for example, is a broad measure based on available rooftop 

area, solar resource, shading, and other factors; by not including all of these factors separately, 

they are not given the same significance in grouping. The energy efficiency score is also a broad 
46 

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measure, but household income was used as a proxy. Ideally, this would be a broad measure of 

wealth, including income, housing prices, cost of living, and other factors. The major 

disadvantages of this method are that it may be difficult to identify an appropriate proxy, and the 

order of group selection alters the results.

Despite the different methods, the resulting groupings appear similar. All but one of the states in 

Groups 1 and 2 ended up in expected leaders and motivated buyers, for example. The main 

differences seem to be some shifting of states that could potentially fit into more than one group. 

Conclusion

This research identified likely factors that drive solar development outside of policy intervention 

using a household-level logistic regression predicting whether a household is heated with solar 

energy. 

Grouping of the states was done to find states with similar market contexts for customer-sited 

solar PV and to inform policy pathways for each specific group. Groups were formed using two 

different strategies—one informed by the results of the regression and the other based on a 

limited set of broad variables informed by previous literature. 

There is some evidence non-policy context influences the effectiveness of policy. It is therefore 

important to define those non-policy contexts so that context-specific policy pathways can be 

developed. This work demonstrated two ways this could be done, but more research may be 

needed to evaluate various groupings in terms of the policy implications that come from them.

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